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Despite achieving satisfactory performance in speaker verification using deepneural networks, variable-duration utterances remain a challenge that threatensthe robustness of systems. To deal with this issue, we propose a speakerverification system called RawNeXt that can handle input raw waveforms ofarbitrary length by employing the following two components: (1) A deep layeraggregation strategy enhances speaker information by iteratively andhierarchically aggregating features of various time scales and spectralchannels output from blocks. (2) An extended dynamic scaling policy flexiblyprocesses features according to the length of the utterance by selectivelymerging the activations of different resolution branches in each block. Owingto these two components, our proposed model can extract speaker embeddings richin time-spectral information and operate dynamically on length variations.Experimental results on the VoxCeleb1 test set consisting of various durationutterances demonstrate that RawNeXt achieves state-of-the-art performancecompared to the recently proposed systems. Our code and trained model weightsare available at https://github.com/wngh1187/RawNeXt.

RawNeXt: Speaker verification system for variable-duration utterances with deep layer aggregation and extended dynamic scaling policies